Marketing’s biggest challenge today isn’t a lack of data — it’s too much of it. Campaigns, channels and customer interactions generate endless metrics, often fragmented across platforms. The decline of third-party cookies and the explosion of new marketing channels — connected TV, retail media, digital out-of-home and more — only deepen this fragmentation.
Critical trends slip through the cracks and decision-making turns reactive. You might spend hours piecing together reports, only to end up with incomplete or conflicting insights that make it hard to trust the numbers.
The challenges of modern marketing measurement
For years, we’ve been looking for a smarter way to manage data overload, but key organizational challenges often stand in the way.
Siloed data streams
Your data is fragmented across platforms and scattered across teams. In large organizations, marketing, sales and customer service often operate in separate systems with different incentives, making it difficult to get a unified view of performance.
Even when technology is available to integrate the data, securing buy-in from cross-functional groups can be a significant challenge. But it’s also an opportunity to foster collaboration and establish shared processes around marketing data. A dedicated project owner or an outside agency can help connect the dots across your organization, improving alignment and efficiency.
Data overload
More data doesn’t always mean better insights. You’ve likely spent time comparing marketing tools, trying to understand why they produce conflicting results.
While using different tools and analyses makes sense for tactical, day-to-day optimizations, you need a unified, holistic solution for strategic decision-making. Without it, you risk getting lost in the noise rather than focusing on what truly moves the needle.
Outdated attribution
The data analytics landscape is shifting fast. With every browser outside of Chrome blocking third-party cookies, traditional attribution models are no longer the one-size-fits-all solution they once were. As user journeys grow more complex and span multiple channels, you have fewer signals to work with.
Attribution still helps optimize single channels and allocate budgets to high-performing campaigns, but relying on it alone won’t give you the whole picture. To truly understand the impact of your marketing efforts, you need a measurement approach that accounts for the broader contribution of each channel.
Privacy challenges
The deprecation of cookies has disrupted the era of “easy tracking,” forcing teams to adapt quickly. However, privacy concerns go beyond technical limitations — new and evolving regulations mean you have access to less data and what you have is more aggregated.
Brands that take privacy seriously will have an edge. Consumers are far more likely to share their data with companies they trust, giving you better signals to optimize your marketing strategies.
Dig deeper: 3 strategies to navigate change as digital privacy evolves
A smarter approach to marketing measurement
Investing in data readiness creates a strong foundation to quickly adapt to next-generation measurement approaches. This means moving beyond outdated optimization methods and siloed metrics. A solid marketing measurement strategy isn’t just about overcoming the limitations of cookie-based tracking — it’s about predicting the optimal media mix for maximum impact.
Dig deeper: Unlocking the power of marketing mix modeling solutions
The evolution of MMM
First used in the 1950s, marketing mix modeling (MMM) is making a comeback as a powerful, privacy-centric method for measuring impact across channels. MMM takes a broader approach than user-level attribution models, which assign credit to specific touchpoints based on observed user journeys. It statistically analyzes the overall impact of your marketing efforts across both online and offline channels.
Despite its potential, traditional MMM had several limitations. It was expensive and provided only a static, yearly analysis that quickly became outdated. It also lacked transparency, making it difficult to understand how results were generated or to customize the model. In short, it just wasn’t actionable enough.
Today, MMM is transforming through advances in cloud computing, data science talent and open-source marketing models. These have made it more cost-effective and capable of delivering continuous, granular insights throughout the year. You can even bring MMM in-house for complete transparency and control. Some machine learning models now offer predictive analysis, forecasting the impact of each channel on your bottom line with regular updates based on past performance. This allows you to optimize media spend across channels for maximum efficiency.
Dig deeper: Rethinking media mix modeling for today’s complex consumer journey
The rise of open-source MMM
Open-source models like Robyn have made advanced measurement more accessible than ever. Recently, Google introduced its own open-source MMM solution, Meridian.
These models provide a low-risk, scalable measurement environment where you can experiment before committing to a fully customized solution. As open-source options continue to evolve, you have more flexibility than ever to test, refine and scale MMM strategies tailored to your business needs.
The race for ROI
MMM is a proven strategy for revenue attribution, and in today’s landscape, proving the impact of every dollar spent is more important than ever. Modern MMM is a game-changer, allowing you to make quick, data-driven decisions about media spend and optimization.
Prioritizing the most efficient marketing levers and improving marketing efficiency
Multinational integrated energy company TotalEnergies (a client of my company, fifty-five) used an internalized MMM to optimize its marketing spend and stay competitive in an evolving digital landscape. Here’s what they did:
- Implemented a classic MMM for a broad view of marketing impact.
- Introduced an agent-based “Digital Twin” model to identify high-return investments through advanced simulations.
- Incorporated a carbon footprint analysis into its platform.
These efforts resulted in $4.1 million in savings, a 20% increase in marketing efficiency and a 20% reduction in the company’s marketing carbon footprint.
Breaking down data silos and managing data overload
Auchan, a major French retailer, improved decision-making by centralizing more than 40 data sources into a unified platform. This provided a clearer view of media investments across digital and offline channels.
The company moved beyond disconnected metrics to actionable insights at both national and local levels by automating data processes and improving measurement consistency. Within a year, these improvements led to a 10% increase in profitability.
Beyond the metrics
Relying on fragmented data or outdated measurement methods creates blind spots, making it harder to connect your marketing efforts to business outcomes. As this new era of measurement takes shape, it’s critical to audit your current approach.
Where are data silos, inconsistent metrics or privacy limitations creating gaps? The goal isn’t just to track more — it’s to build a measurement framework that reflects reality, integrating both online and offline influences for a clear, actionable view of performance.
Dig deeper: Measuring marketing’s impact: From metrics to growth
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